# A Comparison of HDF5, Zarr, and netCDF4 in Performing Common I/O Operations

arXiv: https://arxiv.org/abs/2207.09503 · HTML: https://mlsanity.com/paper/2207.09503/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2022.

## Built on by (most cited first, 1 of 1)

- [Perception of Misalignment States for Sky Survey Telescopes with the Digital Twin and the Deep Neural Networks](https://mlsanity.com/paper/2311.18214/) (2023)

## Abstract (arXiv)

Scientific data is often stored in files because of the simplicity they provide in managing, transferring, and sharing data. These files are typically structured in a specific arrangement and contain metadata to understand the structure the data is stored in. There are numerous file formats in use in various scientific domains that provide abstractions for storing and retrieving data. With the abundance of file formats aiming to store large amounts of scientific data quickly and easily, a question that arises is, "Which scientific file format is best suited for a general use case?" In this study, we compiled a set of benchmarks for common file operations, i.e., create, open, read, write, and close, and used the results of these benchmarks to compare three popular formats: HDF5, netCDF4, and Zarr.